A novel dense spectrum correction algorithm for extracting vibration signals in internal combustion engine and its application

Abstract

The algorithm of dense spectrum correction has been raised and proved based on the correction of discrete spectrum by fast Fourier transform. The result of simulation shows that such algorithm has advantages of high accuracy and small amount of calculation. The algorithm has been successfully applied to the analysis of vibration signals from internal combustion engine. To calculate discrete spectrum, fast Fourier transform has been used to calculate the discrete spectrum by the signals acquired by the sensors on the oil pan, and the signal has been extracted from the mixed signals.

Keywords

fast Fourier transform / discrete spectrum correction / dense spectrum correction / signal extracting

References

[1] ShiL.-s., YuanTao.. Study on blind source separation of vibration signals of IC engine [J]. Transactions of CSICE, 2007, 25(5): 463-468

[2] GroverE. C., LalorN.. A review of low noise diesel engine design at ISVR [J]. Journal of Sound and Vibration, 1973, 28(3): 403-431

[3] LI Xiao, WANG Xin, DONG Da-wei, et al. A new method based on single resonance angular vibration signal for monitoring the operating conditions of internal combustion engine [J]. Diesel Locomotives, 2007 (1): 14–17. (in Chinese)

[4] WongM. L. D., JackL. B., NandiA. K.. Modified self-organising map for automated novelty detection applied to vibration signal monitoring [J]. Mechanical Systems and Signal Processing, 2006, 20(3): 593-610

[5] YangB. S., KwangJ. K.. Application of Dempster-Shafer theory in fault diagnosis of induction motors using vibration and current signals [J]. Mechanical Systems and Signal Processing, 2006, 20(2): 403-420

[6] SheenY. T.. A complex filter for vibration signal demodulation in bearing defect diagnosis [J]. Journal of Sound and Vibration, 2004, 276(1/2): 105-119

[7] XiaY., ZhaoHong.. Application research of wavelet decomposition and image processing for fault diagnosis in internal combustion engines [J]. Journal of Vibration and Shock, 2004, 23(2): 64-67

[8] ZhangJ.-h., ZhaoYong.. Multi-body dynamics analysis of internal combustion engine vibration and noise [J]. China Mechanical Engineering, 2004, 23(2): 64-67

[9] WangY.-l., DaiX.-d., XieY.-bai.. Structural vibration analysis of multi-cylinder engine main bearings [J]. Journal of Xi’an Jiaotong University, 2004, 36(9): 963-967

[10] JinP., ChenY.-r., BaiYe.. Study on features of surface vibration signals of the internal combustion engine [J]. Journal of Tianjin University, 2000, 36(9): 963-967

[11] KazuoE., ShigetakaY., MasatsuguS., AkiraH.. Development and clinical application of a new occlusal force measurement apparatus-wave forms and FFT power spectrum analysis [J]. Pathophysiology, 1998, 4(4): 249-257

[12] KumarU., RidderK. D.. GARCH modelling in association with FFT-ARIMA to forecast ozone episodes [J]. Atmospheric Environment, 2010, 44(34): 4252-4265

[13] DuanH.-m., QinS.-r., LiNing.. A corrective method of a discrete spectrum by extracting frequencies [J]. Journal of Vibration and Shock, 2007, 26(7): 59-75

[14] HangD.-f., XuF., GuoW.-ling.. Harmonic vibration monitoring of rotating machinery using general corrected FFT spectrum [J]. Journal of Nanjing University of Aeronautics & Astronautics, 2004, 36(4): 505-510

[15] HuangD. S.. Phase error in FFT analysis [J]. Mechanical Systems and Signal Processing, 1995, 9(9): 113-118

[16] YangZ.-j., DingK., XuC.-yan.. Simulation of engine excitation force identification based on discrete spectrum correction [J]. Journal of Vibration Engineering, 2010, 23(6): 660-664